from detailed analysis to transient energy management a ... · 1a.x from detailed analysis to...

1A.x From detailed analysis to transient energy management – a novel approach for cabin modelling

1


Jonathon Juszkiewicz, Daniel Marsh, Marek Lehocky, Jan Böbel, Kristian Haehndel 10. Tagung Wärmemanagement des Kraftfahrzeugs, Potsdam 2016

Abstract Effective sizing and control of the HVAC system in a vehicle is a complex and important problem. Designers must balance occupant comfort with energy consumption require-ments. This question has been historically addressed with drive-cycle testing which relies on expensive prototypes and bench tests to reach optimal design. In the last decade, software has become available to help address these concerns at an earlier stage. However, while fast 1D simulations of the refrigerant circuit has become well established among OEMs, cabin studies still rely on time-consuming CFD simulations to provide results usable for comfort assessments. A new software technology has been developed by Gamma Technologies and ThermoAnalytics for modeling the passenger compartment, based on a lower resolution model compared to today’s standards, which still maintains good accuracy. The nature of this new technology results in runtimes that can be faster than real-time and are therefore applicable not only for steady state analysis but also for transient scenarios with a focus on complete vehicle energy management. The aim of this new technology is to deliver a tool that enables a faster and more efficient analysis option during each step of the vehicle development process. This technology is presented and validated with a case study of a vehicle cabin from a major OEM. Multiple scenarios (i.e. heat up, cool down) have been simulated and the results validated against the actual test data.

Kurzfassung

Eine effiziente Auslegung und Regelung des HVAC-Systems in einem Fahrzeug ist ein komplexes und zugleich umfassendes Problem. Designer stehen vor einem Zielkonflikt und müssen Insassenkomfort gegen die Anforderungen hinsichtlich des Energiever-brauchs abwägen. Diese Fragestellung wurde in der Vergangenheit durch aufwändige und kostenintensive Fahrzeugmessungen beantwortet. In den letzten zehn Jahren wurde Software entwickelt, mit deren Hilfe die Problemstellungen bereits zu einem frü-hen Entwicklungszeitpunk analysiert werden können. Während sich schnellrechnende 1D-Simulationen des Kältemittelkreislaufs schnell in der Industrie etabliert haben, ba-siert die Berechnung des Insassenkomforts größtenteils noch immer auf zeitintensiven 3D CFD Berechnungen. Eine neue Methode zur Modellierung des Fahrgastraums wurde von Gamma Techno-logies und ThermoAnalytics gemeinsam entwickelt. Sie basiert auf einem, im Vergleich zu 3D-CFD, niedriger aufgelöstem Modell, welches dennoch eine gute Vorhersage-genauigkeit aufweist. Diese Technologie erlaubt Rechenzeiten schneller als Echtzeit.


2

Damit können nicht nur stationäre Betriebspunkte, sondern auch transiente Fahrzyklen mit einem Fokus auf das Fahrzeuggesamtenergiemanagement, analysiert werden. Das Ziel dieser neuen Technologie ist es, ein Werkzeug zu liefern, das während jeder Phase des Fahrzeugentwicklungsprozesses eine schnellere und effizientere Vorher-sage ermöglicht. Diese Technologie wird anhand einer Fallstudie, welche auf einer OEM-Fahrgastzelle basiert, validiert und vorgestellt. Verschiedene Szenarien (Aufhei-zung, Abkühlung) wurden simuliert und die Ergebnisse anhand aktueller Testdaten validiert.

1 Introduction Improving passenger comfort is considered one of the main design goals when devel-oping a new vehicle. This involves many criteria ranging from the vehicle drivability to noise and vibration levels in the cabin to thermal comfort. On the other hand, energy consumption and exhaust emissions need to be decreased because of stricter legisla-tive regulations. One of the areas where designers have to face this problem is the passenger thermal comfort. Taking a look at the most extreme cases, the vehicle warmup in winter and the cooldown case in summer, one can identify the basic questions that need to be answered. In the winter case there are two main concerns restricting designs. First, the ability of the driver to see through the windows has to be maintained by quickly de-frosting the windscreen and eventually de-humidify the air. Second, to maintain the passenger thermal comfort, the cabin air has to reach the optimal temperature in the shortest possible time. In direct conflict to this is the demand of warming-up the engine parts as fast as possi-ble. Optimal temperature in the engine structure is needed for optimal combustion. Friction losses in the bearings can be minimized when running at correct temperature and the exhaust aftertreatment system needs to reach a certain temperature level to ensure correct functionality. Looking at the summer case the vehicle the cabin needs to be cooled down effectively to maintain optimal passenger comfort. In this case, the A/C compressor demands a significant amount of torque from the engine, therefore increasing the energy con-sumption and in some cases restricting the vehicle drivability. These problems are even more significant when looking at an electrified vehicle where additional heating and cooling limits the driving range. In both described cases, finding the optimal solution is dependent on a large number of factors. In the past, the design process was mainly targeting optimization of a single system. This has proven to be insufficient when focusing on the overall efficiency of the vehicle. Today, all OEMs look into holistic system optimization and use integrated energy management models to improve the system´s efficiency under realistic real world conditions. [1] To be able to efficiently solve the problems, simulation tools are used during the vehicle development process, connecting together the vehicle subsystems to see their respec-tive feedback and real behavior. Those tools range from complex 3D methods trying to capture the basic physical behavior of the systems to 0D models using empirical mathematical equations.


3

More detailed solutions (i.e. 3D) can deliver reliable and detailed results, but require high-performance computers and high simulation times. The less detailed approaches (i.e. 0D) are usually fast, but require precise calibration. This paper presents an approach for coupling two different CAE tools to overcome the trade-off between simulation time and result accuracy for the vehicle cabin subsystem. The goal is to provide a procedure that will allow fast simulation of the cabin thermal behavior while maintaining good accuracy. The method should help cabin designers get results in a shorter time, allowing the running of optimization studies more efficiently and using a detailed physical model for optimizing control strategies. The A/C loop designers on the other hand will benefit from the new approach by getting more accu-rate boundary conditions and clearer design goals for laying out the two-phase circuit. Finally, the cabin and the A/C loop can be simulated together to get a realistic assess-ment of the systems feedback. Integrated simulation allows optimization of both systems not only in the worst-case scenarios, that are usually steady-state, but also by looking at transient behavior of the system and intermediate load cases that may be the most commonly appearing in the product lifecycle. Like described earlier, this integrated simulation requires that the cabin subsystem can be seamlessly integrated and run for longer transient drivecycles.

2 Modeling Procedure One of the most appreciated tools for modeling the thermal behavior of a cabin is the TAITherm (formerly marketed as RadTherm) software. It allows detailed simulation of the conductive heat transfer in the thermal structure, as well as the radiation heat trans-fer that is considered to be one of the key requirements to properly capture cabin cool-down behavior. [2] TAITherm uses a meshed geometry and places a thermal node at the centroid of each element face (front and back surface for a shell part). For modelling Multi-Layer parts, an additional node is placed at the interface between the layers. Using this concept, TAITherm then solves the energy balance equation based on conduction between the nodes, radiation exchange between the surfaces, convection to the fluid and any ad-ditional heat generation. For the solution of radiation heat transfer, view factors are automatically calculated for all elements.

TAITherm currently does not contain a detailed flow solver for capturing the air circu-lation and thermal behavior inside of the cabin. To overcome this problem, 3D CFD tools can be coupled to TAITherm, solving the detailed flow field. This approach is able

Fig 1: Thermal Nodes in TAITherm


4

to give good results, but the computational load introduced by the CFD solution is very high. Recently, works have been published on improving the simulation time on the air flow side, but the approaches were usually restricted on the cabin itself not considering other systems needed for an integrated simulation. To solve this issue a coupling in-terface to the GT-SUITE software was developed. GT-SUITE is widely used for 1D flow simulations in various systems ranging from air distribution systems to two-phase flow in the A/C loop. The 1D solution is based on Navier-Stokes equations, operating on a discretized flow network, the so called stag-gered grid (sometimes referred to as 1D CFD). This approach is able to capture all physical effects like pressure losses and flow distribution in the systems.

Based on the 1D methodology, GT has also developed a quasi-3D flow solution that allows automatic discretization of a 3D volume, being for example a vehicle underhood or a cabin and solving the flow field. Compared to a full 3D CFD simulation, the dis-cretization in GT-SUITE is significantly coarser which allows to speed up the simulation significantly, to nearly or faster than real-time speed.

Fig 3: Discretization of 3D Flow Space in GT-SUITE

The flow solution will take into account the exact shape of the cabin, including seats and other significant geometries, as well as the distribution of flow based on different inlet conditions.

Scalar Variables

• pressure

• enthalpy

• temperature

• etc.

Vector Variables

• mass flux

• velocity

• etc.

Fig 2: Flow Network Discretization and Solution Parameters in GT-SUITE [3]


5

The following section will briefly describe the process of setting up the models in both tools and the coupling procedure. 2.1 Procedure in TAITherm

CAD data representing the geometry is first meshed into quad and tri elements, then imported into the TAITherm software. After assigning the component properties to cor-responding sections of the structure (windows, door structure elements, seats, etc.) the boundary conditions in form of fluid temperatures, air humidity and heat transfer coefficients are imposed, or set up for getting the necessary information from the co-simulation interface.

Fig 4: Example of Thermal Structure Discretization in TAITherm

2.2 Procedure in GT-SUITE GT-SUITE uses the same CAD data as TAITherm but for creating and discretizing the air volume inside the cabin. For this operation, COOL3D, the GT-SUITE pre-pro-cessing tool to discretize large flow volumes, is used. This allows automatic discretiza-tion of the cabin air volume into rectangular-shaped elements taking into account the exact geometry of the cabin. The discretization process converts the 3D geometry into a 1D equivalent flow model which can be solved by GT-SUITE. When the discretized model is loaded into GT-SUITE, the flow boundary conditions need to be defined. This usually contains air flow rates and temperatures on different inlet ducts and pressure on the outlets. Boundary conditions can be defined separately for each inlet duct to model different load cases as the ducts configuration can change between the heat-up and cool-down case as well as when the climate control systems are active. Cabin models can be also directly connected the A/C loop to get the air flow rates and temperatures from the evaporator outlet and to include the feedback of the rest of the refrigerant circuit.


6

During the coupled simulation GT-SUITE solves for the convection heat transfer coef-ficients on the contact surfaces to calculate the heat balance between the wall and the air. For all flow volumes GT-SUITE solves the momentum-, energy- and continuity equation.

Fig 5: Example of Cabin Air Volume Discretization in COOL3D

2.3 Coupling Method

As described in the previous sections, both simulation tools operate in different physi-cal domains and use different geometry discretization. Also the time steps taken for each solution are different. Therefore an appropriate coupling method needs to be used to provide correct communication between the tools. The co-simulation is currently driven by GT-SUITE that imposes a time step to TAITherm and governs the information transfer. The information passed by GT-SUITE contains the temperatures in the fluid volumes in direct contact with the structure as well as the heat transfer coefficients and the air humidity. TAITherm returns the structure temperatures in the centroid of each element in contact with the air. For successful information exchange between the tools, the geometry needs to be mapped correctly and the physical information passed at defined time steps. Most of the work was therefore focused on simplifying the process for the user as much as possible. Automated coupling was developed, taking care of both geometry mapping and imposing of the time steps with minimal user input. As shown in Fig 6, the user input is reduced on defining the geometry files and the communication interval. In the first version, file-based communication was chosen to test the capabilities of the solution method. The communication has proven to be stable and fast, however further improvements and moving to a direct communication interface is planned to accelerate the process.


7

Fig 6: Coupling Template in GT-SUITE

2.3.1 Time Step Synchronization

The thermal inertia of the structure is usually much higher than the inertia of the air. This means that the temperature of the air can change much faster than the tempera-ture of the structure and therefore different time steps can be taken for solving each of the two. The solution time step in GT-SUITE can be about 10-100 times smaller than in TAITherm and this difference needs to be correctly handled by the co-simulation process. The model run is initiated by GT-SUITE with a command specifying the first data ex-change time and the coupling interval. GT-SUITE starts the TAITherm solver and both tools run based on the imposed initial conditions until the first data exchange time is reached. At this point, the calculated boundary conditions (BC) are exchanged. After confirming the successful communication, both tools continue the simulation using the new boundary conditions until the next communication interval is reached. The process is schematically shown in Fig. 7.

Fig 7: Communication Process

2.3.2 Mapping of discretized geometry

Similar as for the time step, the geometry discretization in both tools is different. The structural elements in TAITherm will typically be much finer than the corresponding flow elements in GT-SUITE. This means that one element in GT-SUITE is effectively


8

in contact with multiple elements in TAITherm. The coupling method has to correctly identify the elements in contact and map the boundary conditions accordingly. The surface mapping is done in the initial phase of the co-simulation. During the simulation TAITherm passes information from all its elements set up for co-simulation into GT-SUITE. GT-SUITE then averages the values based on the mapping and imposes them as boundary conditions on its elements. The information from GT-SUITE is directly applied to the mesh used in TAITherm and passed back. This process repeats at each communication interval. An example of the mapping can be seen in Fig. 8. Each TAITherm element and each GT-SUITE cube have a single calculated temperature. The wall temperature for the fluid cube in GT-SUITE will be calculated as an area average from all the TAITherm elements in contact with the cube (inside of the square). In the oposite direction all the elements in TAITherm in contact with the flow cube will get the single air temperature from the cube.

3 Validation Problem For validation of the new coupling methodology a pilot project with Volvo Car was per-formed to test the simulation on the 2012 Volvo S60 geometry. Test data from the climatic tunnel was provided for this vehicle, covering both heat-up and cool-down cases. In the first stage cool-down of the cabin without the solar load was selected as a reference case for the validation. During the test the vehicle is running on three different speeds. Temperatures and flow rates of the air entering the cabin were measured at the active ducts as well as different temperatures of the cabin structure and the air temperatures in different positions. Fig. 9 shows the positions of the air and structure sensors that were used for the vali-dation.

Fig 8: Example of the Mapping: Temperatures


9

Fig 9: Temperature Measurement Sensors

Models in TAITherm and GT-SUITE were prepared based on the detailed geometry data from Volvo Car. In TAITherm the ambient boundary conditions were set to the exterior surface in form of ambient temperature and air velocity based on the vehicle speed. All inner surfaces were set for the co-simulation. In GT-SUITE inlet air boundary conditions were defined as flow rates and temperatures of the air coming into the cabin at different duct positions (Fig. 10).

Fig 10: Example of Boundary Conditions Imposed

3.1 Model Calibration

In some cases calibration of the numerical models in both tools is necessary to account for the simplifications in the solution and unknowns in the geometry and boundary con-ditions. In TAITherm the main calibration factor is the mass of the components that can be modified by adjusting the thickness of the material layers. The mass of the components affects the thermal inertia of the system and hereby the temperature gradients over time. In GT-SUITE heat transfer coefficient multipliers can be applied to calibrate the results. One of the goals of the current work is finding the optimal setup for the calibration factors. In the first tests the driver breath temperature was taken as a reference point for the calibration. One single heat transfer multiplier was applied to the entire cabin to


10

adjust the temperature in the steady-state point to match the data at the end of the test. The mass calibration in TAITherm was not used in this case.

4 Results In Fig. 11 and 12, comparison between the measurement data and simulation is shown corresponding to the locations in Fig 9. For most locations results show a very good correlation to the measured data. The temperature trends as well as the absolute tem-peratures are well-captured. Looking at Fig 10(Pos. 1), faster cooling of the air in the simulation can be observed. This means that the measured temperature is more influenced by a thermal mass in its vicinity than in the simulation. Here the reason probably is the influence of the radi-ation on the sensor, or incorrect position of the sensor in the simulation compared to the measurement. Per discussion with Volvo-Car, the direction of the vent can have a significant impact on the results, and the exact vent direction is not known in the tests. An opposite trend can be seen in the structure temperature in figure 11(Pos. 8). Here the simulated seat structure temperature does not get cooled enough compared to the measurement. As a reason differences in the flow distribution predicted can be identi-fied, leading less cold air into the seat position than necessary. A correction to the flow inlet direction in the simulation model could be made to take this effect into account. For the presented model the simulation time was about 1.1x real-time.

Fig 11: Cabin Air Temperatures


11

Fig 12: Cabin Structure Temperatures

5 Conclusion A new method of coupling TAITherm and GT-SUITE for cabin thermal simulation was presented. The method is based on real CAD geometry data and utilizes the strengths of both tools to achieve a fast and accurate simulation. The method was applied to a model of the Volvo S60 cabin and results were compared to the climatic chamber measurement provided by Volvo Car. The simulation shows good correlation to the test data while the simulation time is reaching the real-time level.

6 Outlook The work on this project will continue by running more sensitivity studies using the available test data. Main goal of the further investigations is to examine the sensitivity of the current calibration and its validity for other load cases. Further, the detailed cabin model will be connected to the full A/C loop and other vehicle systems to test the tran-sient capabilities. Integrated model will be also used for the energy management sim-ulations and to examine usability for the controls development. Improvements to the methodology are also planned by introducing variable time stepping and enhancing the flow-distribution solution. The new method will be available in TAITherm 12.1 and GT-SUITE v2016 b2.


12

Literature [1] Steffens, D.; Finsterwalder, F.; Kemmner, B.; Wärmemanagement des Doppel-

kupplungsgetriebes simuliert in einem energetischen Gesamtfahrzeugmodell. Wärmemangement des Kraftfahzeugs IX, Expert-Verlag, Renningen, 2014

[2] Baumgart, R.; Reduzierung des Kraftstoffverbrauchs durch Optimierung von Pkw-Klimaanlage, Dissertation, Verlag Wissenschaftliche Skripten, 2010

[3] GT-SUITE Manual, Gamma Technologies, LLC, 04/2016

[4] TAITherm Manual, ThermoAnalytics, Inc, 04/2016


13

Autoren / The Authors: Jonathon Juszkiewicz, Thermo Analytics, Detroit Daniel Marsh, Gamma Technologies LLC, Westmont Marek Lehocky, Gamma Technologies GmbH, Stuttgart Jan Böbel, Gamma Technologies GmbH, Stuttgart Dr. Kristian Haehndel, Thermo Analytics GmbH, Munich

from detailed analysis to transient energy management a ... · 1a.x from detailed analysis to...

Documents